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Interlaboratory Attribute Analytics Metrics from the MAM Consortium Round Robin Study



Trina Mouchahoir, John E. Schiel, Rich Rogers, N. Alan Heckert, Benjamin Place, Aaron Ammerman, Xiaoxiao Li, Tom Robinson, Brian Schmidt, Chris M. Chumsae, Xinbi Li, Anton V. Manuilov, Bo Yan, Gregory O. Staples, Da Ren, Alexander J. Veach, Dongdong Wang, Wael Yared, Zoran Sosic, Yan Wang, Li Zang, Anthony M. Leone, Peiran Liu, Richard Ludwig, Li Tao, Wei Wu, Ahmet Cansizoglu, Andrew Hanneman, Greg W. Adams, Irina Perdivara, Hunter Walker, Margo Wilson, Arnd Brandenburg, Nick DeGraan-Weber, Stefano Gotta, Joe Shambaugh, Melissa Alvarez, X. Christopher Yu, Li Cao, Chun Shao, Andrew Mahan, Hirsh Nanda, Kristen Nields, Nancy Nightlinger, Ben Niu, Jihong Wang, Wei Xu, Gabriella Leo, Nunzio Sepe, Yan-Hui Liu, Bhumit A. Patel, Douglas Richardson, Yi Wang, Daniela Tizabi, Oleg V. Borisov, Yali Lu, Ernest L. Maynard, Albrecht Gruhler, Kim F. Haselmann, Thomas N. Krogh, Carsten P. Sönksen, Simon Letarte, Sean Shen, Kristin Boggio, Keith Johnson, Wenqin Ni, Himakshi Patel, David Ripley, Jason C. Rouse, Ying Zhang, Carly Daniels, Andrew Dawdy, Olga Friese, Thomas W. Powers, Justin B. Sperry, Josh Woods, Eric Carlson, K. Ilker Sen, St John Skilton, Michelle Busch, Anders Lund, Martha Stapels, Xu Guo, Sibylle Heidelberger, Harini Kaluarachchi, Sean McCarthy, John Kim, Jing Zhen, Ying Zhou, Sarah Rogstad, Xiaoshi Wang, Jing Fang, Weibin Chen, Ying Qing Yu, John G. Hoogerheide, Rebecca Scott, Hua Yuan


The multi-attribute method (MAM) was conceived as a single assay to potentially replace multiple single-attribute assays that have long been used in process development and quality control (QC) for protein therapeutics. MAM is rooted in traditional peptide mapping methods; it leverages mass spectrometry (MS) detection for confident identification and quantitation of many types of protein attributes that may be targeted for monitoring. While MAM has been widely explored across the industry, it has yet to gain a strong foothold within QC laboratories as a replacement method for established orthogonal platforms. Members of the MAM consortium recently undertook an interlaboratory study to evaluate the industry-wide status of MAM. Here we present the results of this study as they pertain to the targeted attribute analytics component of MAM, including investigation into the sources of variability between laboratories and comparison of MAM data to orthogonal methods. These results are made available with an eye toward aiding the community in further optimizing the method to enable its more frequent use in the QC environment.
Journal of the American Society for Mass Spectrometry


attribute analytics, multi-attribute method, MAM Consortium, targeted analytics, NISTmAb


Mouchahoir, T. , Schiel, J. , Rogers, R. , Heckert, N. , Place, B. , Ammerman, A. , Li, X. , Robinson, T. , Schmidt, B. , Chumsae, C. , Li, X. , Manuilov, A. , Yan, B. , Staples, G. , Ren, D. , Veach, A. , Wang, D. , Yared, W. , Sosic, Z. , Wang, Y. , Zang, L. , Leone, A. , Liu, P. , Ludwig, R. , Tao, L. , Wu, W. , Cansizoglu, A. , Hanneman, A. , Adams, G. , Perdivara, I. , Walker, H. , Wilson, M. , Brandenburg, A. , DeGraan-Weber, N. , Gotta, S. , Shambaugh, J. , Alvarez, M. , Yu, X. , Cao, L. , Shao, C. , Mahan, A. , Nanda, H. , Nields, K. , Nightlinger, N. , Niu, B. , Wang, J. , Xu, W. , Leo, G. , Sepe, N. , Liu, Y. , Patel, B. , Richardson, D. , Wang, Y. , Tizabi, D. , Borisov, O. , Lu, Y. , Maynard, E. , Gruhler, A. , Haselmann, K. , Krogh, T. , Sönksen, C. , Letarte, S. , Shen, S. , Boggio, K. , Johnson, K. , Ni, W. , Patel, H. , Ripley, D. , Rouse, J. , Zhang, Y. , Daniels, C. , Dawdy, A. , Friese, O. , Powers, T. , Sperry, J. , Woods, J. , Carlson, E. , Sen, K. , Skilton, S. , Busch, M. , Lund, A. , Stapels, M. , Guo, X. , Heidelberger, S. , Kaluarachchi, H. , McCarthy, S. , Kim, J. , Zhen, J. , Zhou, Y. , Rogstad, S. , Wang, X. , Fang, J. , Chen, W. , Yu, Y. , Hoogerheide, J. , Scott, R. and Yuan, H. (2022), Interlaboratory Attribute Analytics Metrics from the MAM Consortium Round Robin Study, Journal of the American Society for Mass Spectrometry, [online],, (Accessed June 22, 2024)


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Created August 26, 2022, Updated November 29, 2022